Social Entropy-Based Question Selection for KBA Security
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Solution Overview
Problem
Conventional Knowledge-Based Authentication (KBA) methods are vulnerable to identity spoofing due to the use of questions with low social entropy, making it easy for others to guess or discover answers, thereby compromising account security.
Innovation Solution
A system and method for selecting questions based on social entropy, which involves identifying potential questions, determining the availability of information about the correct answers, calculating social entropy, and selecting questions with high entropy to enhance identity verification, thereby reducing the likelihood of illegitimate access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional KBA questions are used (e.g., maiden name of mother), then the KBA process is simple to implement, but the security and reliability are compromised due to low entropy and ease of guessing
Solution Approach 1:
The system performs preliminary analysis of social media information and calculates entropy values for potential KBA questions before the authentication process. This advance preparation allows the system to pre-identify high-entropy questions that are difficult to guess, thereby improving reliability without adding complexity during the actual authentication moment
Solution Approach 2:
The patent introduces an intermediary entropy calculation mechanism that bridges conventional KBA questions and security requirements. By using social media data as an intermediary layer to assess question entropy, the system selects questions that balance simplicity with high security, resolving the contradiction between easy implementation and reliable verification
2Reliability
If questions with low entropy are used, then the KBA process is easier to operate, but the security is weakened as answers can be easily guessed or discovered by others
Solution Approach 1:
The system changes the parameter of question selection from static conventional questions to dynamic high-entropy questions based on social media analysis. By calculating entropy values and selecting questions above a threshold, the system transforms the parameter of question difficulty to achieve both high security and operational feasibility
3Measurement precision
If high entropy questions are selected, then the security and accuracy of identity verification improve, but the complexity of determining and calculating entropy increases
Solution Approach 1:
The system uses publicly available social media data to self-assess the entropy of potential KBA questions. By leveraging existing social media information rather than requiring external databases or complex external systems, the patent enables the system to autonomously calculate entropy and select appropriate questions, improving measurement precision while managing complexity through self-service
Data Source
AI summary
The disclosed computer-implemented method for selecting questions for knowledge-based authentication based on social entropy may include (1) identifying a potential question to ask a user of a computing system during a KBA process in an attempt to verify the user's identity, (2) determining whether any information suggestive of a correct answer to the potential question is available to anyone other than the user of the computing system, (3) calculating a social entropy of the potential question based at least in part on the determination of whether any information suggestive of the correct answer is available to anyone other than the user, and then (4) selecting the potential question to be asked to the user during the KBA process based at least in part on the social entropy of the potential question. Various other methods, systems, and computer-readable media are also disclosed.